An Improved Multi-Objective Hybrid Genetic-Simulated Annealing Algorithm for AGV Scheduling under Composite Operation Mode

This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.

FCC coprocessing oil sands heavy gas oil and canola oil. 2. Gasoline hydrocarbon type analysis

This study set out to gain a deeper understanding of a fluid catalytic cracking(FCC)coprocessing approach using canola oil mixed with bitumen-derived heavy gas oil(HGO),for the production of partially-renewable gasoline,with respect to its composition and quality.The FCC coprocessing approach may provide an alternative solution to reducing the carbon footprint and to meet government regulatory demands for renewable transportation fuels.In this study,a mixture of 15 v%canola oil in HGO was catalytically cracked with a commercial equilibrium catalyst under typical FCC conditions.Cracking experiments were performed using a bench-scale Advanced Cracking Evaluation(ACE)unit at a fixed weight hourly space velocity of 8 h^(à1),490–530C,and catalyst/oil ratios of 4–12 g/g.The total liquid product samples were injected via an automatic sampler and a prefractionator(to removet254C)into a gas chromatographic system containing a series of columns,traps,and valves designed to separate each of the hydrocarbon types.The analyzer gives detailed hydrocarbon types of à200C gasoline,classified into paraffins,iso-paraffins,olefins,naphthenes,and aromatics by carbon number up to C_(11)(C_(10)for aromatics).For a feed cracked at a given temperature,the gasoline aromatics show the highest selectivity in terms of weight percent conversion,followed by saturated iso-paraffins,saturated naphthenes,unsaturated iso-paraffins,unsaturated naphthenes,unsaturated normal paraffins,and saturated normal paraffins.As conversion increases,both aromatics and saturated iso-paraffins increase monotonically at the expense of other components.Hydrocarbon type analysis and octane numbers with variation in feed type,process severity(temperature and catalyst/oil ratio),and conversion are also presented and discussed.